Unverified50% confidenceFactExact time
Large language models cannot provide human-auditable reasoning traces, making it difficult for researchers to determine after the fact where conclusions came from
1
Sources
50%
Confidence
Long-term
Relevance
9/28/2026
First Seen
Sources
Related Entities
Related Claims
Unverified现有大语言模型基本停留在因果阶梯的第一层，无法真正执行干预和反事实的因果推理77% similarUnverifiedCritics point out that when a problem is one for which even humans cannot provide a reference proof, the formal steps generated by LLMs still lack guidance, and the system as a whole remains unable to break through the boundaries of knowledge.75% similarUnverifiedPure vector retrieval can only measure semantic proximity and cannot establish explicit logical associations between entities, making it difficult to handle cross-document comprehensive reasoning problems.74% similarUnverifiedAI is unable to judge the significance of research and cannot distinguish which conjectures are at the cutting-edge core and which are outdated, repetitive conclusions.74% similarUnverifiedThe output of large models is probabilistic and cannot be simply judged for equality, so they cannot be tested using traditional assertion methods.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/957150API
curl https://kongchang.com/api/v1/knowledge/claims/957150MCP
get_claim(id=957150)